Overview

Brought to you by YData

Dataset statistics

 Raw_FeatBinned_Feat
Number of variables88
Number of observations56255625
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows0969
Duplicate rows (%)0.0%17.2%
Total size in memory357.2 KiB486.2 KiB
Average record size in memory65.0 B88.5 B

Variable types

 Raw_FeatBinned_Feat
Numeric74
Categorical14

Alerts

Raw_FeatBinned_Feat
churn is highly overall correlated with customer_service_callschurn is highly overall correlated with customer_service_callsHigh Correlation
customer_service_calls is highly overall correlated with churncustomer_service_calls is highly overall correlated with churnHigh Correlation
churn is highly imbalanced (57.3%) churn is highly imbalanced (57.3%) Imbalance
customer_happiness has unique values Alert not present in this datasetUnique
customer_service_calls has 2813 (50.0%) zeros customer_service_calls has 2985 (53.1%) zeros Zeros
Alert not present in this dataset Dataset has 969 (17.2%) duplicate rowsDuplicates
Alert not present in this datasettotal_day_charge has 938 (16.7%) zeros Zeros
Alert not present in this datasetcustomer_happiness has 686 (12.2%) zeros Zeros

Reproduction

 Raw_FeatBinned_Feat
Analysis started2024-09-11 11:15:35.9964162024-09-11 11:15:38.779108
Analysis finished2024-09-11 11:15:38.7740312024-09-11 11:15:40.224826
Duration2.78 seconds1.45 second
Software versionydata-profiling vv4.10.0ydata-profiling vv4.10.0
Download configurationconfig.jsonconfig.json

Variables

total_day_minutes
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct15038
Distinct (%)26.7%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean902.913426.4581333
 Raw_FeatBinned_Feat
Minimum00
Maximum22007
Zeros4646
Zeros (%)0.8%0.8%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-09-11T13:15:40.441478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum00
5-th percentile291.25
Q16546
median9037
Q311417
95-th percentile15157
Maximum22007
Range22007
Interquartile range (IQR)4871

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation363.526110.86462714
Coefficient of variation (CV)0.402614590.1338819
Kurtosis-0.1203814424.541603
Mean902.913426.4581333
Median Absolute Deviation (MAD)2440
Skewness0.045615971-3.9044124
Sum507888836327
Variance132151.240.74758009
MonotonicityNot monotonicNot monotonic
2024-09-11T13:15:40.547915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
0.8%
926 15
 
0.3%
941 14
 
0.2%
1071 13
 
0.2%
853 13
 
0.2%
1022 12
 
0.2%
831 12
 
0.2%
671 12
 
0.2%
945 11
 
0.2%
955 11
 
0.2%
Other values (1493) 5466
97.2%
ValueCountFrequency (%)
7 3196
56.8%
6 2108
37.5%
5 226
 
4.0%
0 46
 
0.8%
4 37
 
0.7%
3 6
 
0.1%
2 5
 
0.1%
1 1
 
< 0.1%
ValueCountFrequency (%)
0 46
0.8%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
21 1
 
< 0.1%
27 2
 
< 0.1%
29 1
 
< 0.1%
ValueCountFrequency (%)
0 46
 
0.8%
1 1
 
< 0.1%
2 5
 
0.1%
3 6
 
0.1%
4 37
 
0.7%
5 226
 
4.0%
6 2108
37.5%
7 3196
56.8%
ValueCountFrequency (%)
0 46
 
0.8%
1 1
 
< 0.1%
2 5
 
0.1%
3 6
 
0.1%
4 37
 
0.7%
5 226
 
4.0%
6 2108
37.5%
7 3196
56.8%
ValueCountFrequency (%)
0 46
0.8%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
21 1
 
< 0.1%
27 2
 
< 0.1%
29 1
 
< 0.1%

total_day_charge
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct36006
Distinct (%)64.0%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean44.6534492.4995556
 Raw_FeatBinned_Feat
Minimum00
Maximum98.415
Zeros4938
Zeros (%)0.1%16.7%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-09-11T13:15:40.685779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum00
5-th percentile19.470
Q134.531
median44.642
Q354.714
95-th percentile68.9145
Maximum98.415
Range98.415
Interquartile range (IQR)20.183

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation15.0418571.708081
Coefficient of variation (CV)0.336857680.68335388
Kurtosis-0.036956693-1.2684478
Mean44.6534492.4995556
Median Absolute Deviation (MAD)10.091
Skewness0.0363995430.00065149344
Sum251175.6514060
Variance226.257462.9175407
MonotonicityNot monotonicNot monotonic
2024-09-11T13:15:40.759816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.81 6
 
0.1%
48.04 6
 
0.1%
56.03 6
 
0.1%
42.73 6
 
0.1%
43.96 5
 
0.1%
44.8 5
 
0.1%
57.66 5
 
0.1%
39.88 5
 
0.1%
58.81 5
 
0.1%
50.76 5
 
0.1%
Other values (3590) 5571
99.0%
ValueCountFrequency (%)
2 939
16.7%
5 938
16.7%
0 938
16.7%
3 937
16.7%
1 937
16.7%
4 936
16.6%
ValueCountFrequency (%)
0 4
0.1%
1.02 1
 
< 0.1%
1.13 1
 
< 0.1%
2.22 1
 
< 0.1%
2.87 1
 
< 0.1%
3.19 1
 
< 0.1%
3.77 2
< 0.1%
3.8 1
 
< 0.1%
4.08 1
 
< 0.1%
4.71 1
 
< 0.1%
ValueCountFrequency (%)
0 938
16.7%
1 937
16.7%
2 939
16.7%
3 937
16.7%
4 936
16.6%
5 938
16.7%
ValueCountFrequency (%)
0 938
16.7%
1 937
16.7%
2 939
16.7%
3 937
16.7%
4 936
16.6%
5 938
16.7%
ValueCountFrequency (%)
0 4
0.1%
1.02 1
 
< 0.1%
1.13 1
 
< 0.1%
2.22 1
 
< 0.1%
2.87 1
 
< 0.1%
3.19 1
 
< 0.1%
3.77 2
< 0.1%
3.8 1
 
< 0.1%
4.08 1
 
< 0.1%
4.71 1
 
< 0.1%
 Raw_FeatBinned_Feat
Distinct9774
Distinct (%)17.4%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
695
 
20
604
 
20
648
 
19
776
 
19
497
 
18
Other values (972)
5529 
1
3149 
2
1719 
0
648 
3
 
109

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5625
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Raw_FeatBinned_Feat
Unique1640 ?
Unique (%)2.9%0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
695 20
 
0.4%
604 20
 
0.4%
648 19
 
0.3%
776 19
 
0.3%
497 18
 
0.3%
556 18
 
0.3%
566 17
 
0.3%
435 17
 
0.3%
0 17
 
0.3%
564 17
 
0.3%
Other values (967) 5443
96.8%
ValueCountFrequency (%)
1 3149
56.0%
2 1719
30.6%
0 648
 
11.5%
3 109
 
1.9%

Length

2024-09-11T13:15:40.827646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Raw_Feat


Number of variable categories passes threshold (config.plot.cat_freq.max_unique)

Binned_Feat

2024-09-11T13:15:40.879800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3149
56.0%
2 1719
30.6%
0 648
 
11.5%
3 109
 
1.9%

Most occurring characters

ValueCountFrequency (%)
1 3149
56.0%
2 1719
30.6%
0 648
 
11.5%
3 109
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3149
56.0%
2 1719
30.6%
0 648
 
11.5%
3 109
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3149
56.0%
2 1719
30.6%
0 648
 
11.5%
3 109
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3149
56.0%
2 1719
30.6%
0 648
 
11.5%
3 109
 
1.9%
 Raw_FeatBinned_Feat
Distinct3155
Distinct (%)5.6%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
126
 
48
165
 
46
167
 
46
190
 
45
177
 
44
Other values (310)
5396 
1
2352 
2
2173 
0
627 
3
448 
4
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5625
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Raw_FeatBinned_Feat
Unique270 ?
Unique (%)0.5%0.0%

Sample

1st row0
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
126 48
 
0.9%
165 46
 
0.8%
167 46
 
0.8%
190 45
 
0.8%
177 44
 
0.8%
144 44
 
0.8%
158 43
 
0.8%
157 43
 
0.8%
174 43
 
0.8%
140 42
 
0.7%
Other values (305) 5181
92.1%
ValueCountFrequency (%)
1 2352
41.8%
2 2173
38.6%
0 627
 
11.1%
3 448
 
8.0%
4 25
 
0.4%

Length

2024-09-11T13:15:40.943320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Raw_Feat


Number of variable categories passes threshold (config.plot.cat_freq.max_unique)

Binned_Feat

2024-09-11T13:15:40.994412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2352
41.8%
2 2173
38.6%
0 627
 
11.1%
3 448
 
8.0%
4 25
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1 2352
41.8%
2 2173
38.6%
0 627
 
11.1%
3 448
 
8.0%
4 25
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2352
41.8%
2 2173
38.6%
0 627
 
11.1%
3 448
 
8.0%
4 25
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2352
41.8%
2 2173
38.6%
0 627
 
11.1%
3 448
 
8.0%
4 25
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2352
41.8%
2 2173
38.6%
0 627
 
11.1%
3 448
 
8.0%
4 25
 
0.4%
 Raw_FeatBinned_Feat
Distinct114
Distinct (%)0.2%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
8
1084 
7
1026 
10
900 
9
896 
6
822 
Other values (6)
897 
3
2880 
2
1848 
1
859 
0
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5625
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Raw_FeatBinned_Feat
Unique00 ?
Unique (%)0.0%0.0%

Sample

1st row3
2nd row1
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
8 1084
19.3%
7 1026
18.2%
10 900
16.0%
9 896
15.9%
6 822
14.6%
5 489
8.7%
4 265
 
4.7%
3 105
 
1.9%
2 25
 
0.4%
1 10
 
0.2%
ValueCountFrequency (%)
3 2880
51.2%
2 1848
32.9%
1 859
 
15.3%
0 38
 
0.7%

Length

2024-09-11T13:15:41.046804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Raw_Feat


Number of variable categories passes threshold (config.plot.cat_freq.max_unique)

Binned_Feat

2024-09-11T13:15:41.090117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2880
51.2%
2 1848
32.9%
1 859
 
15.3%
0 38
 
0.7%

Most occurring characters

ValueCountFrequency (%)
3 2880
51.2%
2 1848
32.9%
1 859
 
15.3%
0 38
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2880
51.2%
2 1848
32.9%
1 859
 
15.3%
0 38
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2880
51.2%
2 1848
32.9%
1 859
 
15.3%
0 38
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2880
51.2%
2 1848
32.9%
1 859
 
15.3%
0 38
 
0.7%

customer_happiness
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct56258
Distinct (%)100.0%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.504660523.5393778
 Raw_FeatBinned_Feat
Minimum0.000179391010
Maximum0.999868717
Zeros0686
Zeros (%)0.0%12.2%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-09-11T13:15:41.144342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum0.000179391010
5-th percentile0.0536147480
Q10.253094372
median0.510565774
Q30.757669956
95-th percentile0.951410397
Maximum0.999868717
Range0.999689327
Interquartile range (IQR)0.504575584

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation0.290661982.3058898
Coefficient of variation (CV)0.575955450.65149583
Kurtosis-1.2269888-1.2546137
Mean0.504660523.5393778
Median Absolute Deviation (MAD)0.252789632
Skewness-0.0096845972-0.011237124
Sum2838.715419909
Variance0.0844843865.317128
MonotonicityNot monotonicNot monotonic
2024-09-11T13:15:41.211247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.183440548 1
 
< 0.1%
0.1182925103 1
 
< 0.1%
0.5159505222 1
 
< 0.1%
0.6489132417 1
 
< 0.1%
0.5419121075 1
 
< 0.1%
0.6339349819 1
 
< 0.1%
0.582825159 1
 
< 0.1%
0.9452413572 1
 
< 0.1%
0.8098898999 1
 
< 0.1%
0.6851322974 1
 
< 0.1%
Other values (5615) 5615
99.8%
ValueCountFrequency (%)
7 752
13.4%
2 738
13.1%
4 710
12.6%
5 705
12.5%
6 698
12.4%
1 696
12.4%
0 686
12.2%
3 640
11.4%
ValueCountFrequency (%)
0.0001793910098 1
< 0.1%
0.0002135787151 1
< 0.1%
0.0004282800256 1
< 0.1%
0.0007159080322 1
< 0.1%
0.001564124626 1
< 0.1%
0.001667041497 1
< 0.1%
0.001753145611 1
< 0.1%
0.002242134096 1
< 0.1%
0.002280608135 1
< 0.1%
0.002336968566 1
< 0.1%
ValueCountFrequency (%)
0 686
12.2%
1 696
12.4%
2 738
13.1%
3 640
11.4%
4 710
12.6%
5 705
12.5%
6 698
12.4%
7 752
13.4%
ValueCountFrequency (%)
0 686
12.2%
1 696
12.4%
2 738
13.1%
3 640
11.4%
4 710
12.6%
5 705
12.5%
6 698
12.4%
7 752
13.4%
ValueCountFrequency (%)
0.0001793910098 1
< 0.1%
0.0002135787151 1
< 0.1%
0.0004282800256 1
< 0.1%
0.0007159080322 1
< 0.1%
0.001564124626 1
< 0.1%
0.001667041497 1
< 0.1%
0.001753145611 1
< 0.1%
0.002242134096 1
< 0.1%
0.002280608135 1
< 0.1%
0.002336968566 1
< 0.1%

customer_service_calls
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct946
Distinct (%)1.7%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean11.9969781.5424
 Raw_FeatBinned_Feat
Minimum00
Maximum1115
Zeros28132985
Zeros (%)50.0%53.1%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-09-11T13:15:41.280876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum00
5-th percentile00
Q100
median00
Q3203
95-th percentile504
Maximum1115
Range1115
Interquartile range (IQR)203

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation17.5330871.7869319
Coefficient of variation (CV)1.46145861.1585398
Kurtosis2.5048174-1.3635539
Mean11.9969781.5424
Median Absolute Deviation (MAD)00
Skewness1.65744380.52589399
Sum674838676
Variance307.409133.1931255
MonotonicityNot monotonicNot monotonic
2024-09-11T13:15:41.348043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2813
50.0%
1 100
 
1.8%
3 89
 
1.6%
6 88
 
1.6%
15 80
 
1.4%
5 80
 
1.4%
7 72
 
1.3%
2 72
 
1.3%
12 71
 
1.3%
10 71
 
1.3%
Other values (84) 2089
37.1%
ValueCountFrequency (%)
0 2985
53.1%
4 947
 
16.8%
3 835
 
14.8%
2 445
 
7.9%
5 270
 
4.8%
1 143
 
2.5%
ValueCountFrequency (%)
0 2813
50.0%
1 100
 
1.8%
2 72
 
1.3%
3 89
 
1.6%
4 54
 
1.0%
5 80
 
1.4%
6 88
 
1.6%
7 72
 
1.3%
8 69
 
1.2%
9 65
 
1.2%
ValueCountFrequency (%)
0 2985
53.1%
1 143
 
2.5%
2 445
 
7.9%
3 835
 
14.8%
4 947
 
16.8%
5 270
 
4.8%
ValueCountFrequency (%)
0 2985
53.1%
1 143
 
2.5%
2 445
 
7.9%
3 835
 
14.8%
4 947
 
16.8%
5 270
 
4.8%
ValueCountFrequency (%)
0 2813
50.0%
1 100
 
1.8%
2 72
 
1.3%
3 89
 
1.6%
4 54
 
1.0%
5 80
 
1.4%
6 88
 
1.6%
7 72
 
1.3%
8 69
 
1.2%
9 65
 
1.2%

churn
Categorical

 Raw_FeatBinned_Feat
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size49.6 KiB178.6 KiB
0
5135 
1
 
490
0
5135 
1
 
490

Length

 Raw_FeatBinned_Feat
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 Raw_FeatBinned_Feat
Total characters56255625
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Raw_FeatBinned_Feat
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Raw_FeatBinned_Feat
1st row00
2nd row00
3rd row00
4th row11
5th row00

Common Values

ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Length

2024-09-11T13:15:41.407756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Raw_Feat

2024-09-11T13:15:41.511884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:41.557321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5625
100.0%
ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5625
100.0%
ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5625
100.0%
ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Interactions

Raw_Feat

2024-09-11T13:15:38.342518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.932028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:36.164363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:38.947312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:36.602659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.133464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:36.965297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.378041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.709857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:38.024287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.697062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:38.386736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.979332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:36.260366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:38.995134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:36.646638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.179067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:37.021940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.422344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.753722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:38.068221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.744989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:38.429694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:36.335894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:36.689225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.134575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.470165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.796899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:38.113560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:38.476490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:36.405718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:36.742931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.184975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.518767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.845834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:38.162333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:38.524964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:36.468384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:36.805097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.236872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.568830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.894261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:38.211176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:38.570569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:40.021783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:36.514447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.039562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:36.856312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.221022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:37.284355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.615172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.937471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:38.254935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.789259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:38.613938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:40.066735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:36.559035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.087105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:36.910375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.267488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-09-11T13:15:37.331191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.663465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:37.980817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat


Interaction plot not present for dataset

Raw_Feat

2024-09-11T13:15:38.298680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:39.885717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

Raw_Feat

2024-09-11T13:15:41.589472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-09-11T13:15:41.663140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

churncustomer_happinesscustomer_service_callscustomer_service_ratingtotal_day_chargetotal_day_minutestotal_eve_callstotal_eve_minutes
churn1.0000.2390.5890.2210.0000.0180.0070.020
customer_happiness0.2391.000-0.005-0.0250.0130.0240.0140.002
customer_service_calls0.589-0.0051.0000.0270.003-0.004-0.001-0.004
customer_service_rating0.221-0.0250.0271.000-0.001-0.007-0.002-0.033
total_day_charge0.0000.0130.003-0.0011.0000.021-0.027-0.004
total_day_minutes0.0180.024-0.004-0.0070.0211.000-0.0120.001
total_eve_calls0.0070.014-0.001-0.002-0.027-0.0121.000-0.007
total_eve_minutes0.0200.002-0.004-0.033-0.0040.001-0.0071.000

Binned_Feat

churncustomer_happinesscustomer_service_callscustomer_service_ratingtotal_day_chargetotal_day_minutestotal_eve_callstotal_eve_minutes
churn1.0000.2360.6110.2210.0170.0370.0240.020
customer_happiness0.2361.000-0.0050.0250.0130.0130.0040.021
customer_service_calls0.611-0.0051.0000.013-0.000-0.0080.0000.000
customer_service_rating0.2210.0250.0131.0000.0000.0000.0240.000
total_day_charge0.0170.013-0.0000.0001.0000.0200.0140.007
total_day_minutes0.0370.013-0.0080.0000.0201.0000.0100.023
total_eve_calls0.0240.0040.0000.0240.0140.0101.0000.031
total_eve_minutes0.0200.0210.0000.0000.0070.0230.0311.000

Missing values

Raw_Feat

2024-09-11T13:15:38.672814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.

Binned_Feat

2024-09-11T13:15:40.123462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.

Raw_Feat

2024-09-11T13:15:38.740894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Binned_Feat

2024-09-11T13:15:40.190298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Raw_Feat

total_day_minutestotal_day_chargetotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn
497788395.060.53608.06190.18344100
69163782.049.06610.014430.01019500
3463421144.029.49549.018380.71049800
592071148.046.84560.0163100.040356511
682551941.065.91438.099100.260329190
5374071123.037.28510.024380.55055060
9660101102.026.83526.010990.33366100
3447651277.046.70846.011760.01223410
4065461496.051.42417.018270.459110390
797390823.050.18765.024070.22113490

Binned_Feat

total_day_minutestotal_day_chargetotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn
49778865103100
6916363111000
34634270123500
5920773123051
68255175113230
53740771133420
96601070113200
34476573212000
40654674122340
79739063232120

Raw_Feat

total_day_minutestotal_day_chargetotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn
4727771073.067.53802.0258100.89548000
440487937.048.28489.016290.672675370
243139583.023.73452.0153100.54389800
557988485.035.58637.018670.44086800
857974945.035.05514.0154100.40712300
914712909.034.97405.09390.25021400
559243606.063.27987.017150.94971300
77144584.036.39641.08480.448140130
1916121336.024.68713.021790.00322901
650330345.033.16625.025360.69700600

Binned_Feat

total_day_minutestotal_day_chargetotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn
47277775233700
44048773123540
24313960113400
55798861122300
85797471113300
91471271113200
55924365221700
7714461113330
19161270223001
65033061132500

Duplicate rows

Raw_Feat

total_day_minutestotal_day_chargetotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn# duplicates
Dataset does not contain duplicate rows.

Binned_Feat

total_day_minutestotal_day_chargetotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn# duplicates
4027011300010
8937511300010
734731237009
99611222007
146621131007
277641132007
397701122007
420701223007
431701236007
492711131007